Associate Professor in Machine Learning

Considerations on Treatments in a Causal Analysis of Covid19 Data

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In this post we consider again the problem of performing causal analysis on covid19 data, specifically the question How is the implementation of existing strategies affecting the rates of COVID-19 infection?.

We restrict our attention only to treatments variables that may figure in a causal model designed to answer such a question. We review measures reported in the HSE dataset, and we study how they combine to form manifold treatments. We point out the challenges of trying to set up such an analysis. The main point of this notebook is to illustrate a preliminary setup for causal analysis.

Notebook here

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